Proteolytic susceptibility of food by-product proteins: An evaluation by means of a quantitative index

Enzymatic Hydrolysis of Salmon Frame Proteins Using a Sequential Batch Operational Strategy: An Improvement in Water-Holding Capacity

The meat industry uses phosphates to improve the water-holding capacity (WHC) of meat products, although excess phosphates can be harmful to human health. In this sense, protein hydrolysates offer an alternative with scientific evidence of improved WHCs. Salmon frames, a byproduct rich in protein, must be processed for recovery. Enzymatic technology allows these proteins to be extracted from muscle, and the sequential batch strategy significantly increases protein nitrogen extraction. This study focused on evaluating the WHC of protein hydrolysates from salmon frames obtained through double- and triple-sequential batches compared to conventional hydrolysis. Hydrolysis was carried out for 3 h at 55 °C with 13 mAU of subtilisin per gram of salmon frames. The WHC of each hydrolysate was measured as the cooking loss using concentrations that varied from 0 to 5% (w/w) in the meat matrix. Compared with those obtained through conventional hydrolysis, the hydrolysates obtained through the strategy of double- and triple-sequence batches demonstrated a 55% and 51% reduction in cooking loss, respectively, when they were applied from 1% by weight in the meat matrix. It is essential to highlight that all hydrolysates had a significantly lower cooking loss (p ≤ 0.05) than that of the positive control (sodium tripolyphosphate [STPP]) at its maximum allowable limit when applied at a concentration of 5% in the meat matrix. These results suggest that the sequential batch strategy represents a promising alternative for further improving the WHC of hydrolysates compared to conventional hydrolysis. It may serve as a viable substitute for polyphosphates.

Modeling Tool for Studying the Influence of Operating Conditions on the Enzymatic Hydrolysis of Milk Proteins

Systematic modeling of the enzymatic hydrolysis of milk proteins is needed to assist the study and production of partially hydrolyzed milk. The enzymatic hydrolysis of milk proteins was characterized and evaluated as a function of the temperature and protease concentration using Alcalase, Neutrase and Protamex. Modeling was based on the combination of two empirical models formed by a logarithmic and a polynomial equation to correlate the kinetic constants and the operating conditions. The logarithmic equation fitted with high accuracy to the experimental hydrolysis curves with the three proteases (R2 > 0.99). The kinetic constants were correlated with the operating conditions (R2 > 0.97) using polynomial equations. The temperature and protease concentration significantly affected the initial rate of hydrolysis, i.e., the kinetic constant a, while the kinetic constant b was not significantly affected. The values for the kinetic constant a were predicted according to the operating conditions and they were strongly correlated with the experimental data (R2 = 0.95). The model allowed for a high-quality prediction of the hydrolysis curves of milk proteins. This modeling tool can be used in future research to test the correlation between the degree of hydrolysis and the functional properties of milk hydrolysates.

Modelling of the kinetics of red tilapia (Oreochromis spp.) viscera enzymatic hydrolysis using mathematical and neural network models

The present work modelled the enzymatic hydrolysis of red tilapia (Oreochromis spp.) viscera with Alcalase® 2.4 L in both 0.5 and 5 L reactors. The best conditions for the enzymatic hydrolysis were 60°C and pH 10. The product inhibited the enzymatic hydrolysis, and the enzyme deactivated following second-order reaction. K_M and K_p from a secondary plot of K_M^app as a function of inhibitor concentration, and k_2, p, and k_3 were found by non-linear regression. While the obtained parameters modelled the 0.5 L reactor well, it did not model the 5 L reactor, probably because of unconsidered fluid dynamics in the model. To have a better modelling, a neural network (tensorflow.keras.models module) was built and trained. The neural network modelled the enzymatic hydrolysis of red tilapia at several concentrations of substrate and enzyme. This result proved that neural networks are a powerful tool for modelling biological processes.

Application of high-resolution ultrasonic spectroscopy for real-time monitoring of trypsin activity in β-casein solution

High-resolution ultrasonic spectroscopy (HR-US) was applied for real-time monitoring of β-casein hydrolysis by trypsin at various conditions for the first time. The technique is based on the precision measurement of hydration changes proportional to the number of peptide bond hydrolyzed. As HR-US exhibits ultrasonic transparency for most solution, the analysis did not require optical transparency like for 2,4,6-trinitrobenzenesulfonic acid (TNBS) assay. Appropriate enzymatic models were fitted with degree of hydrolysis (d_h) profiles to provide kinetic and mechanistic description of proteolysis in terms of initial hydrolysis rate, r⁰, and rate constant of hydrolysis, k_h, and enzyme inactivation, k_d. Maximal r⁰ and d_h were obtained at 45 °C and pH 8. The exponential dependence of kinetic parameters allowed determination of the activation (E_A = 50.3 ± 7 kJ/mol) and deactivation (E_D = 62.23 ± 3 kJ/mol) energies of hydrolysis. The ultrasonic assay provided rapid detection of trypsin activity even at sub-nanomolar concentration.